R help - Nov 2013 - How should I specify partially crossed random effects in lme?

Dear all,
I am new to R and would like your help with lme formula for partially
crossed random effect in a random-intercept, random-slope model.
In the longitudinal data I have, each subject (barring some dropouts) was
tested at 5 different occasions. The standardized tests were administered
by 3 different examiners, with 2 of them present at all occasions, and the
3rd one administering tests only on the last two occasions. The subjects
were randomly assigned to the examiners.
I tested the following models:
model1<-lme(score~time*covariate,random=~time|subject,method="REML",na.action=na.omit,data=dat)
model2<-lme(score~time*covariate,random=list(examiner=~1,subject=~time),method="REML",na.action=na.omit,data=dat)

anova(model1,model2) gives p<0.05 with better model fit for model2.

I would like to know if model2 is the correct way to specify the partially
crossed random effect in the data I described.

thanks!
Pradeep Babu

	[[alternative HTML version deleted]]

You are more likely to get a useful response on the r-sig-mixed-models
list rather than here.

Cheers,
Bert

On Sat, Nov 23, 2013 at 9:04 PM, Pradeep Babu S. <pradeep.babu at
gmail.com> wrote:
> Dear all,
> I am new to R and would like your help with lme formula for partially
> crossed random effect in a random-intercept, random-slope model.
> In the longitudinal data I have, each subject (barring some dropouts) was
> tested at 5 different occasions. The standardized tests were administered
> by 3 different examiners, with 2 of them present at all occasions, and the
> 3rd one administering tests only on the last two occasions. The subjects
> were randomly assigned to the examiners.
> I tested the following models:
>
model1<-lme(score~time*covariate,random=~time|subject,method="REML",na.action=na.omit,data=dat)
>
model2<-lme(score~time*covariate,random=list(examiner=~1,subject=~time),method="REML",na.action=na.omit,data=dat)
>
> anova(model1,model2) gives p<0.05 with better model fit for model2.
>
> I would like to know if model2 is the correct way to specify the partially
> crossed random effect in the data I described.
>
> thanks!
> Pradeep Babu
>
>         [[alternative HTML version deleted]]
>
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

(650) 467-7374